Facebook Partners with NYU School of Medicine to use AI which accelerates the Time Taken to Produce Results of MRI Scans

The FastMRI project will leverage a de-identified imaging dataset collected by NYU that includes 10,000 clinical cases and about 3 million magnetic resonance images.

A new collaborative research targeted at leveraging artificial intelligence to make magnetic resonance imaging scans 10 times faster is being initiated by NYU School of Medicine and social media giant Facebook.

The program known as fastMRI is meant to accelerate notoriously slow MRI machines which usually take 15 minutes to over an hour to conduct scans to less than a second or as long as a minute for Xray and CT scans respectively.

“This project will initially focus on changing how MRI machines operate,” wrote Larry Zitnick from the Facebook Artificial Intelligence Research group and NYU School of Medicine’s Michael Recht, MD, and Daniel Sodickson, MD, in a blog announcing the initiative. “Using AI, it may be possible to capture less data and therefore scan faster, while preserving or even enhancing the rich information content of magnetic resonance images. The key is to train artificial neural networks to recognize the underlying structure of the images in order to fill in views omitted from the accelerated scan.NYU’s Center for Advanced Imaging Innovation and Research has been working with the Facebook Artificial Intelligence Research group on the enabling technology.

While Facebook data will not be used as part of the project, fastMRI will leverage a de-identified imaging dataset collected by NYU that includes 10,000 clinical cases and about 3 million magnetic resonance images of the knee, brain and liver.

As the project progresses, researchers say Facebook will share the AI models, baselines and evaluation metrics with the broader research community, while the NYU School of Medicine will open-source the image dataset.

“We believe the fastMRI project will demonstrate how domain-specific experts from different fields and industries can work together to produce the kind of open research that will make a far-reaching and lasting positive impact in the world,” the researchers concluded, including ultra-low-dose CT scans suitable for vulnerable populations such as pediatric patients.

“With the goal of radically changing the way medical images are acquired in the first place, our aim is not simply enhanced data mining with AI, but rather the generation of fundamentally new capabilities for medical visualization to benefit human health,” they added.